# Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

arXiv: https://arxiv.org/abs/1701.00008 · HTML: https://mlsanity.com/paper/1701.00008/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2016.

## Built on by (most cited first, 24 of 24)

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- [Learning and Interpreting Gravitational-Wave Features from CNNs with a Random Forest Approach](https://mlsanity.com/paper/2505.20357/) (2025)
- [Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis](https://mlsanity.com/paper/2507.11192/) (2025)
- [Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks](https://mlsanity.com/paper/2606.13941/) (2026)
- [AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity](https://mlsanity.com/paper/2607.01372/) (2026)
- [The AI-Enabled Scientific Frontier](https://mlsanity.com/paper/2609.16258/) (2026)

## Abstract (arXiv)

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams. We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks. We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers. Our results indicate that Deep Filtering significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster thus allowing real-time processing of raw big data with minimal resources. More importantly, Deep Filtering extends the range of gravitational wave signals that can be detected with ground-based gravitational wave detectors. This framework leverages recent advances in artificial intelligence algorithms and emerging hardware architectures, such as deep-learning-optimized GPUs, to facilitate real-time searches of gravitational wave sources and their electromagnetic and astro-particle counterparts.